Why Enterprises Need To Focus On Decentralized AI

2020: Year of Worldwide Data Crisis?

In 2009 when we wrote our paper on cloud computing we worried deeply about the state of the cloud computing industry from a global energy perspective. So we proposed an extensible architecture in which we propose to connect the APIs (Power/MDM APIs, Data Center APIs and Consumption-based Open Standards API, etc.) through and through the whole value chain.

Out of our concern for the energy crisis which we foresaw back then, I wrote:

With more and more Data Centers being built, and soon after we are done bashing the economy and again ready to make hay as the economy starts turning around, we can alter the shape of our destiny dramatically!

As other industries will sag, think of hotels, airlines, etc., which will certainly contribute to a certain reduction of carbon emissions, there is strong reason to believe that the spin-offs from the remote-everything will go into overdrive.

Today we are carefully talking about online conferences and soon there will be lots of online activities that will be firing up all over the place.

This will lead to huge data crunch operations as more and more information will need more processing power as it will come in audio, video and other formats.

This will be extremely demanding for data centers, no matter how centralized they are. The data center owners will have to realize that their continuous dependence on energy suppliers and other crises shouldn’t lead to a financial market-like collapse.

There is again strong reason to believe that the inter-dependence will also foster co-creation and co-conservation but it could also lead to frictions when it’s time for “someone to pay.”

We had focused our paper on exactly addressing the same problem. We tried to combine the “industrialized metering solutions”; something the electrical grids have come to do in a reasonable and sustainable fashion, while still making enough profit.

The need to standardize and meter the data centers and eventually charge the consumer with an RTP/RTB (Real-Time Pricing/Real-Time Billing) mechanism which is state-approved — for the same reason I keep calling it RCC or Regulated Cloud Computing.

Hmmm, we proposed regulated cloud computing and I am not really sure if that happened but one thing did happen for sure!

A lot of data was created and data-hungry companies like Facebook, Google and Twitter were growing like weeds!

Everybody was thrilled with the prospects of what this could mean. Washington was celebrating entrepreneurship and inviting 20 something’s to the world stage.

More about that a bit later.

Let’s look back at the predictions I made in 2009!

It’s still on as we haven’t touched 2020 yet

Play along with me to see how I foresaw it in 2009 and how I was so horribly wrong (worse news is coming!).

2012 – How the data hunger began

2014 – How it started accelerating. The more we gave our data, the more drunk they got.

2016 – Things get tense, Twitter struggles (I had projected that it would kill Facebook, which obviously didn’t happen).

2018 – Data Centers become increasingly unsustainable, ecological disasters start to happen (this has not started happening yet, but data breaches are raising their ugly head).

2020 – Crash in full swing. Political Fallout. War?

Well, let’s hope it doesn’t get to that!

But wait! The reality today is even uglier

These internet companies went on collecting data in the most unregulated manner in massive volumes, praying at the “church of AI” on how great it is to share data and the more you share the more you know “because our algorithms do magic, you know.”

Letting them monitor your every move – be it from “tracking your eyeballs” or simply watching you do things if you used their devices, seemed like a pretty good deal because we got free services in return.

No one was complaining. Well you’d think – “we’re still in safer waters, it’s my neighbor’s life that is being disrupted or completely destroyed by a Facebook misadventure or other malicious activity such as sharing private photo’s online, it’s not mine.”

Sooner or later you start getting a feeling that a more sinister play is underway when you start realizing that they know a lot more about you than you can imagine. Worse yet is the problem enterprises are facing today yet fail to truly recognize – where the constant dismantling or deconstruction of their superstructure is coming from. It’s coming from these very internet companies; it’s not a direct attack but its tremors are being felt in boardrooms, the workforce and slowly but steadily on their balance sheets.

All that data is being collected in their data centers which are also called “Siren Servers.” Whether it is your digital life or even corporate workforce (the majority of these people also work at corporate enterprises). Ok, let’s not start hallucinating assuming these companies have the best interest of citizens in their minds and just focus on more obvious challenges. These servers essentially lead to centralization of your non-core applications which you’d rather have someone else run.

But things have changed with the advent of AI, you cannot afford to have your IP, your DNA, your lifeblood rest on some server outside your control, supervision or premises. The AI economy will be defining many enterprises that will eventually suffer and die if they don’t pay attention.

Siren servers are like blackholes. They are leading to an unsustainable situation economically (it’s centralizing financial power), labor-wise (it’s accumulating talent to its core leaving academia and intellectuals chasing large salaries), and ethically (well, if every decision about what is going to happen with your data is made by some 20 something sitting in Silicon Valley then we have as huge problem!)

Facebook – I did try to persuade Mark Zuckerberg back in 2014

Let’s talk a bit about Facebook in particular.

I had applied for a VP role for APAC in 2014 and I thought of suggesting to Mark Zuckerberg (through my presentation narrative) that they urgently needed to move to serving enterprises and consumers by focusing on more meaningful products and services such as Oculus. I stated that the platform is basically a random hack and brings the worst out of people especially when they don’t have meaningful things to do. Create immersive services that could help transform followers into friends who trust you and who you can trust.

Currently we are just pouring our thoughts and life in pictures and videos, all meaninglessly.

This random wandering and wasting time on the platform can result in depression amongst teenagers, and it’s affecting people on a psychological level. We’re only beginning to scratch the surface here.

As it turns out, this didn’t happen. Over 110 million people’s digital lives were sold or stolen (as they say), manipulated and misguided. Well, the folks at “Hackerway” sure did hack your life!

Also point to note, as an enterprise you may not care that while your employees waste away millions of hours on social media they are not focusing on productive work. Ask yourself this if you’re a CEO:

If you are a CEO, you should be seriously asking… Are my employees working for Facebook (essentially because they are a product) while I pay them their paycheck? If you are not asking this then your shareholders should tighten the ropes and ask this to the executive board.

Google – Cloud Servers and Great Open Source Software

It’s tempting when you are in a frenzy of sharing all your knowledge to an “open source project.” You feel involved, your solution and contribution is important and you are a “valued” member of the community who gets a cool sticker “Google Contributor” at conferences.

While the developer community has worked tirelessly to bring so many cool solutions to folks via open source it still needs to be examined if this is truly helping enterprises. Questions like these need to be continually discussed by CIOs, CFOs and CEOs:

Can we build the same solution in-house with inexpensive hardware and open source software?

What are the long-term risks in trying to offload our AI related projects from IP, algorithms and our future product roadmap perspective?

What about GDPR for enterprises? Are we well equipped to ask these enterprises how they are using their data?

Do we understand the “game on the internet” well enough? According to U.S Anti-Trust authorities GDPR seems to be helping the same companies it set out to tame!

The key message here is that you need to be ahead in understanding how AI works and how it can work in your favour and not others.

Why build a decentralized AI?

Corporates need to have their own #AIplaybook, something which is free from vendor seductions and lovely software and hardware solutions. Today we have come around the whole circle again. High Performance Computing is cheap and you can purchase it for your eco-friendly server rooms and Data Centers.

Basically it’s about addressing three problems that are plaguing the industry:

AI solutions today are completely centralized across their entire lifecycle. From training to deployment and optimization, AI systems today are dependent on centralized authorities that operate under explicit trust boundaries and control the data and resources needed to implement a specific AI solution. From that perspective, AI systems today are influenced by at least four centralization vectors.

A lot has been written about the risks of centralized AI models. In my opinion, the key risks of AI centralization can be summarized in three simple principles:

The Rich Get Richer Problem: Centralized AI today is mostly a privilege of large companies with rich datasets. These companies are able to hire expensive data science teams that develop models which produce more data which enriches the company’s datasets. This vicious cycle has resulted in companies such as Google, Apple, Facebook and Amazon acquiring unprecedented levels of knowledge and influence in our daily lives.

The Decentralized Knowledge-Centralized AI Friction: From a cognitive standpoint, knowledge acquisition is an intrinsically decentralized activity. When mastering a task, we instinctively tend to get inputs from different sources. However, we insist on imposing a single source of authority over most AI systems today which consistently leads to biased and incomplete knowledge.

The Transparency-Influence Ratio: Centralized AI systems are influencing a disproportionate ratio of the level of influence that large organizations have over their customers/users and their levels of transparency. Never in the history of mankind so few people known so much about so many people without any obligations to be transparent about their knowledge. Without getting political, just compare the level of influence that political campaigns launched on Google, Twitter or Facebook can have in the outcome of an election with the level of understanding that the general population has about how those companies process data and acquire knowledge about users.

A new hardware revolution is coming from an unexpected corner!

We at deepkapha.ai (disclaimer: I am CEO of deepkapha.ai) are partnering with some niche startups that are defining the micro-DC, mini-DC architecture with solutions that can throw some light into the reinvention of your internal enterprise. With edge computing principles you can place these mini-data centers in your car if you are an automotive enterprise, on lampposts and other locations if you are a local municipality or law enforcement agency in your own country, or in general if you are an enterprise that no longer wishes to share its IP and Custom-Built algorithms with these “Siren Server” owners.

I guess I’ve said enough!

Conclusion

Nation-states and corporate enterprises will have to agree upon this should they want to conserve the depleting energy resources and misused data assets of its citizens and workers and regulate the internet companies that are on a mad rush to serve generic, centralized AI to one and all.

Warning remains that if we don’t do this, we may not be heading for a data center crash as I predicted in 2009 but a huge data disaster that may lead to citizen dissent, enterprise backlash and regulatory authorities slapping frequent and exorbitant penalties.